In the financial services industry, we can see financial organizations adopting cognitive analytics tools, to do things, such as investigate transactions, trace funds, recall funds, issue audit certificates and even manage funds disbursements for construction loan mortgages
Imagine that you called your bank’s customer care number and a machine responded to you in your preferred language, understood your problem and solved it immediately – much quicker and more accurately than a customer care representative could have. This is the power of cognitive analytics at work and could well be the future of customer engagement.
Cognitive analytics allow a precise and immediate analysis of behavioral characteristics in different environments. This leads to a more personalized and satisfying experience for the customer – be it a product recommendation or a query response or an investment option. Cognitive tools working largely with unstructured data as inputs (e.g. email and documents) have the ability to learn from experience, use reason and take decisions by understanding natural languages.
In the financial services industry, we can see financial organizations adopting cognitive analytics tools, to do things, such as investigate transactions, trace funds, recall funds, issue audit certificates and even manage funds disbursements for construction loan mortgages.
Banks are using cognitive capabilities to analyze large volumes of unstructured and structured data (like research reports, products prevalent in the market and externally available customer profiles) to unearth connections between customers’ needs and investment options and weigh various financial options available to customers. In response to questions posed in natural language, cognitive systems can present hypotheses, reasoned arguments and recommendations that are unique to the interaction.
For example, large retail banks in India have started using humanoid robots in its key branches which interacts with customers walking in branches in natural language and give assistance directly without intervention of real people.
Uncovering hidden connections
Another reason for adopting cognitive analytics is to uncover hidden connections within data which are not easily identified. A good example would be the complex transactions involving institutional clients. These transactions are usually complex and involve approvals related to credit, market risk, compliance issues and regulatory policies. By the time a team gets to grips with the data, the market dynamics might have changed, making their efforts redundant. On the other hand, a cognitive system can rapidly absorb trading and compliance policies, regulatory documents, and appropriate risk calculations and offer recommendations relating to the trade even when dynamics are changing.
Clearly, a cognitive analytics tool provides more personalised support to customers. Automating processes will allow businesses to have a lower human resource cost. For companies that have suffered from their processes to have disappeared into a ‘black box’ from outsourcing, this is an opportunity to take back the knowledge in-house.
Cognitive analytics will also pose some challenges for financial institutions. The risks of automating roles and processes are mostly the same as the risks posed by traditional outsourcing. The problems of appropriate function allocation, mode errors, and misuse of automation will continue to challenge system safety and efficiency. As more and more roles are automated, the knowledge of writing algorithms/programming robots will be valuable in itself, which implies a shift in how we value knowledge.
The more automation as a phenomenon goes forward, the greater the risk that organizations need to completely revise their view on what kind of skills that are important. For companies that have not previously been accustomed to external cooperation, the requirement of transparency is often a major adjustment. The biggest challenge perhaps today is to bridge the gap between demand and supply of skills with proper knowledge of cognitive technologies.
Hence, the key for organizations is to be conscious about what processes are automated. The processes that can really give an organization a competitive advantage should be kept internal and not automated. The big question is: Which ones?Vivek Belgavi, Leader – Fintech, PwC India and Sakya Dasgupta, Associate Director, PwC India